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    <h4><b>David A. Wells</b></h4>
    <p class="w3-text-grey">Biostatistician and AI developer</p>
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    <h1><b>About Me</b></h1>
    <p>👋 Hi, I’m @DAWells
🦠 I’m interested in virus meta/genome assembly, vaccine development, and evolution in general.
👨‍💻 I'm using bioinformatics and machine learning.</p>
	    
    <p>Here I post useful snippets of code, demonstrations, and explorations of statistical techniques. 
	    If you would like to contact me please get in touch through <a href="https://www.linkedin.com/in/david-wells-811444185/">linkedIn</a>.</p>
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		  <a href="https://www.kdnuggets.com/2022/04/optimizing-genes-genetic-algorithm.html">
			  <img src="https://www.kdnuggets.com/wp-content/uploads/wells_optimizing_genes_genetic_algorithm_2022_4.png" alt="Algorithmic DNA" style="width:100%" class="w3-hover-opacity">
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			  <p><b>Codon pair optimisation</b></p>
			  <p>Optimising genes with a genetic algorithm.</p>
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		  <a href="https://github.com/DAWells/DAWells.github.io/blob/master/analyses/categorical_cmaps.ipynb">
			  <img src="" alt="Confusion matrix" style="width:100%" class="w3-hover-opacity">
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			  <p><b>Matplotlib cmap</b></p>
			  <p>Simple demos of matplotlib's cmaps.</p>
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			  <p><b>ggtree samples</b></p>
			  <p>Code snippets for various ggtree examples.</p>
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			  <p><b>Flu subtypes</b></p>
			  <p>How is the flu sequence data distributed across subtypes, hosts and countries on the NCBI data base?</p>
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			  <p><b>Confusion matrix</b></p>
			  <p>When reporting binary classification models, just looking at accuracy doesn't give the full picture. You need sensitivity & specificity, or precision & recall.</p>
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			  <p><b>ggtree samples</b></p>
			  <p>Code snippets for various ggtree examples.</p>
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			  <img src="pictures/ordinal_prob.png" alt="Ordinal responses" style="width:100%" class="w3-hover-opacity">
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			  <p><b>Ordinal GLM</b></p>
			  <p>When a response variable is ordered categories you can predict all categories using a single proportional odds model, instead of separate logistic regression models.</p>
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			  <p><b>Phylogenetic mixed modelling</b></p>
			  <p>Simulate data for a phylogenetic mixed model using a phylogenetic tree to impose the phylogenetic covariance structure.</p>
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			  <p><b>Gaussian mixture</b></p>
			  <p>Unsuperivsed classification based on bivariate data using a gaussian mixture model.</p>
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			  <p><b>Power & resolution</b></p>
			  <p>When binning a continuous variable, e.g. into high and low, positive and negative, you lose information and statistical power.</p>
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			  <p><b>Additive mixed modelling</b></p>
			  <p>Fitting an additive mixed model to account for both long an short term effects of time while fitting an autocorrelation structure to the residuals.</p>
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			  <p><b>Gaussian process</b></p>
			  <p>Fitting a gaussian process to time series data. Using this approach domain knowledge can be used to choose an appropriate covariance function.</p>
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        <p>Web scraping, analysing, and interactive visualisations of data on scientific publishing.</p>
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			  <p>Easily turn your code, workflow, and results into a presentation. This is great for sharing results with or without your code.</p>
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